1. Field of the Invention
The present invention relates generally to data processing and, more specifically, to object-oriented programming systems and processes.
2. Description of the Related Art
The phrase "case-based reasoning" (CBR) generally refers to a computer process that finds solutions to current problems by examining descriptions of similar, previously encountered problems and their associated solutions, matching the novel problems to the closest previously encountered problems, and using the associated solutions to produce a solution to the current problem. In a CBR system, problem-solution descriptions are stored in a database called a case base. Weights assigned to different properties of each case are used in scoring cases for similarity against a current problem. The CBR system receives a description of a current problem. retrieves the closest matching cases from the case base using a query engine processor, and iteratively prompts the user for additional descriptive information until the retrieved case or cases are sufficiently close (similar) to be considered a solution to the current problem. The produced solution is then validated through a variety of means, such as user feedback or automatic validation. A validated solution can be added to the case base and used in future problem solving, if appropriate.
CBR systems permit experience gained from solving problems to be applied to a much larger number of problem situations than could possibly be remembered by any one individual with substantially reduced chance of providing erroneous or inconsistent solutions. Validation of problem solutions provides an additional safeguard. Finally, updating the case base permits continuous expansion of the case base against which problems are matched, reducing the likelihood that a satisfactory problem solution cannot be produced. In practical terms, CBR systems are gaining use in computer-assisted and automated help-desk and customer service systems and in computer help programs.
The construction of the case base and the way in which stored cases are matched and retrieved can vary greatly from CBR system to CBR system. For example, the case base can comprise problem-solution descriptions stored as plain language text, data records having predefined fields, or semantic networks. Case matching and retrieval processing by the query engine can comprise implementation of nearest-neighbor algorithms, decision trees, or associative memories. The user interface also must be part of the CBR system development process, including construction of the user input and solution presentation mechanisms. The development of each CBR system therefore can require much time, effort, and expense in making such data representation and processing, decisions and implementing them.
Many CBR systems are tailored for each particular subject matter application and are developed using conventional procedure-oriented programming languages, such as FORTRAN, Pascal, and C. Many lines of computer programming code must be created for each application. For example, even if the same type of case-matching, query engine processing is used for different CBR systems, the query engine must be adapted to work with the problem-solution description being used. Some code can be modified from other versions, or deleted and replaced with different instructions for different applications, which still can require much analysis and design effort. The development of CBR systems would be easier, less expensive, and less time consuming if the user interface could be more consistent, representation of the problem-solution descriptions standardized, development of the case base made simpler, and the query engine made interchangeable from application to application.
As CBR systems become more widely distributed, more users of CBR systems will be novice users who might be unfamiliar with the case base and with CBR search techniques generally. With current CBR search implementations, search techniques employed by novice users can easily be relatively ineffective. For example, CBR systems typically use weighted matching techniques to find the cases in the case base that are the closest match to a set of specified search criteria. Cases are defined by a set of properties and cases are deemed more closely matched to a search query if they have more properties in common. Conventional weighting techniques can be said to penalize cases that are more completely defined (have more properties in their definition) because searches that leave many properties unspecified result in fewer completely-defined cases being deemed a match. Novice users are especially prone to leaving search properties unspecified.
Many CBR search techniques also penalize cases that are under-defined, or that have fewer properties defined than are defined in the search query. Users of the CBR system have no control over the way in which the system deals with unspecified properties. Some systems attempt to reduce such problems by assigning different weights to properties and thereby properly accommodate case properties that are not specified in a search, case properties that are unmatched to a search, or search properties that are unmatched to a case. Specifying the case base can become relatively complicated, as case base developers struggle to assign weights. Moreover, different weights might be advisable, depending on the specific search. The CBR system either becomes unresponsive to different searches or case definition becomes too complex.
In addition, it would be advantageous to permit both case base developers and CBR system users to include their knowledge about the relative importance of properties for each search query and to adjust the match scoring approach. That is, a CBR system user should not be forced to use the weight set specified by the case base developer. Unfortunately, a CBR system that also supports input of weight values for each search query has not been available.
From the discussion above, it should be apparent that there is a need for a case-based reasoning system development mechanism tool that provides a basis for more rapid, less expensive, and simpler development of case-based reasoning systems with greater user flexibility. The present invention satisfies this need.